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import os
import time
import numpy as np
import torch
from typing import BinaryIO, Union, Tuple, List
import faster_whisper
from faster_whisper.vad import VadOptions
import ctranslate2
import whisper
import gradio as gr
from argparse import Namespace
from modules.whisper.whisper_parameter import *
from modules.whisper.whisper_base import WhisperBase
class FasterWhisperInference(WhisperBase):
def __init__(self,
model_dir: str,
output_dir: str,
args: Namespace
):
super().__init__(
model_dir=model_dir,
output_dir=output_dir,
args=args
)
self.model_paths = self.get_model_paths()
self.device = self.get_device()
self.available_models = self.model_paths.keys()
self.available_compute_types = ctranslate2.get_supported_compute_types(
"cuda") if self.device == "cuda" else ctranslate2.get_supported_compute_types("cpu")
def transcribe(self,
audio: Union[str, BinaryIO, np.ndarray],
progress: gr.Progress,
*whisper_params,
) -> Tuple[List[dict], float]:
"""
transcribe method for faster-whisper.
Parameters
----------
audio: Union[str, BinaryIO, np.ndarray]
Audio path or file binary or Audio numpy array
progress: gr.Progress
Indicator to show progress directly in gradio.
*whisper_params: tuple
Parameters related with whisper. This will be dealt with "WhisperParameters" data class
Returns
----------
segments_result: List[dict]
list of dicts that includes start, end timestamps and transcribed text
elapsed_time: float
elapsed time for transcription
"""
start_time = time.time()
params = WhisperParameters.as_value(*whisper_params)
if params.model_size != self.current_model_size or self.model is None or self.current_compute_type != params.compute_type:
self.update_model(params.model_size, params.compute_type, progress)
segments, info = self.model.transcribe(
audio=audio,
language=params.lang,
task="translate" if params.is_translate and self.current_model_size in self.translatable_models else "transcribe",
beam_size=params.beam_size,
log_prob_threshold=params.log_prob_threshold,
no_speech_threshold=params.no_speech_threshold,
best_of=params.best_of,
patience=params.patience,
temperature=params.temperature,
compression_ratio_threshold=params.compression_ratio_threshold,
)
progress(0, desc="Loading audio..")
segments_result = []
for segment in segments:
progress(segment.start / info.duration, desc="Transcribing..")
segments_result.append({
"start": segment.start,
"end": segment.end,
"text": segment.text
})
elapsed_time = time.time() - start_time
return segments_result, elapsed_time
def update_model(self,
model_size: str,
compute_type: str,
progress: gr.Progress
):
"""
Update current model setting
Parameters
----------
model_size: str
Size of whisper model
compute_type: str
Compute type for transcription.
see more info : https://opennmt.net/CTranslate2/quantization.html
progress: gr.Progress
Indicator to show progress directly in gradio.
"""
progress(0, desc="Initializing Model..")
self.current_model_size = self.model_paths[model_size]
self.current_compute_type = compute_type
self.model = faster_whisper.WhisperModel(
device=self.device,
model_size_or_path=self.current_model_size,
download_root=self.model_dir,
compute_type=self.current_compute_type
)
def get_model_paths(self):
"""
Get available models from models path including fine-tuned model.
Returns
----------
Name list of models
"""
model_paths = {model:model for model in whisper.available_models()}
faster_whisper_prefix = "models--Systran--faster-whisper-"
existing_models = os.listdir(self.model_dir)
wrong_dirs = [".locks"]
existing_models = list(set(existing_models) - set(wrong_dirs))
webui_dir = os.getcwd()
for model_name in existing_models:
if faster_whisper_prefix in model_name:
model_name = model_name[len(faster_whisper_prefix):]
if model_name not in whisper.available_models():
model_paths[model_name] = os.path.join(webui_dir, self.model_dir, model_name)
return model_paths
@staticmethod
def get_device():
if torch.cuda.is_available():
return "cuda"
elif torch.backends.mps.is_available():
return "auto"
else:
return "cpu"
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